Electric Speedster Jordan High School

Science Olympiad Electric Vehicle (2025–2026)

This repository contains the control code and supporting utilities for a Science Olympiad Electric Vehicle designed for precision distance travel, repeatability, and rule-compliant autonomy.

The system emphasizes:

• deterministic motion control
  
• accurate distance targeting
  
• consistent braking behavior
  
• modular tuning for different track conditions
  

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Overview

The Electric Vehicle event requires a battery-powered vehicle to travel a target distance as accurately as possible and stop within a designated scoring zone. This project focuses on software-driven precision, combining sensor feedback, calibrated motion profiles, and iterative testing.

Core design goals:

• minimize run-to-run variance
  
• decouple tuning parameters from logic
  
• allow fast adjustments between competition runs
  

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Features

• Distance-based motion control
  • encoder-driven or time-based travel (configurable)
• Predictable stopping behavior
  • controlled deceleration and braking logic
• Parameterized configuration
  • speed, ramping, cutoff distance, and brake timing are easily adjustable
• Fail-safe handling
  • conservative defaults to avoid overshoot
• Lightweight architecture
  • optimized for microcontroller constraints

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Hardware Assumptions

This codebase is written to be hardware-agnostic but assumes:

• a DC motor drivetrain
  
• motor driver with PWM control
  
• wheel encoders or calibrated timing constants
  
• microcontroller compatible with Arduino-style toolchains
  

(Exact pin mappings and constants should be updated for your specific build.)

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Tuning Workflow

1. Calibrate distance
   • determine encoder ticks or time per unit distance
2. Set cruise speed
   • high enough for consistency, low enough for braking margin
3. Adjust cutoff point
   • when the vehicle transitions from drive → brake
4. Iterate
   • log results and refine parameters between runs

Most improvements come from reducing variance, not increasing speed.

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Attribution

This project is adapted from control logic originally developed by Dillon Mehta and Jay Sahni for the 2024 Science Olympiad Robot Tour event, which placed 2nd overall at a national-level invitational. The root code can be found in the following repo: https://github.com/DillonMehta/autonomusPathfindingBot

The code was subsequently reworked, extended, and adapted by Dillon Mehta for use in his 2025–2026 Science Olympiad Electric Vehicle, which he currently competes with. Modifications reflect the Electric Vehicle event’s distinct constraints, scoring mechanics, and emphasis on braking precision and distance repeatability.

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Disclaimer

This repository is intended for educational and competition use.
Users are responsible for ensuring compliance with the official Science Olympiad rules for their competition year.

Built With

• c++